Systems and methods for estimating a time of an engine event

1. A system for estimating when an engine event occurs, comprising:

at least one knock sensor coupled to a combustion engine;

at least one engine crankshaft sensor coupled to the combustion engine;

an engine control unit configured to:

receive a first signal from the at least one knock sensor;

receive a second signal from the at least one engine crankshaft sensor;

transform the first signal from the at least one knock sensor into a plurality of feature vectors using a multivariate transformation algorithm by converting the first signal into a spectrogram and transforming the spectrogram into the plurality of feature vectors;

sort, using the second signal from the at least one engine crankshaft sensor, the plurality of feature vectors into a first state and a second state, wherein the first state corresponds to a time before the engine event and the second state corresponds to a time after the engine event; and

form a first statistical model using the first state;

form a second statistical model using the second state;

combine the first statistical model and the second statistical model into a third statistical model;

determine, using the third statistical model, an expected timing window of the engine event;

center a segment of the plurality of feature vectors around the expected timing window by associating a frequency pattern indicative of the engine event with the first signal from the at least one knock sensor;

estimate, using the third statistical model, an estimated time in the expected timing window corresponding to the engine event; and

sending a signal to take corrective action based on the estimated time of the engine event.

2. The system of claim 1 , wherein the engine event lasts 100 milliseconds (“ms”) or less.

3. The system of claim 1 , wherein the engine event is a valve closure.

4. The system of claim 1 , wherein the engine event is a mechanical failure of the combustion engine.

5. The system of claim 1 , wherein the third statistical model is a Gaussian Mixture Model.

6. The system of claim 1 , wherein the third statistical model is a machine learning algorithm.

7. The system of claim 1 , wherein the multivariate transformation algorithm is a short time Fourier Transform.

8. The system of claim 1 , wherein the third statistical model estimates the time of the engine event by determining a sequence of feature vectors that comprises a maximum likelihood of the engine event occurring.

9. The system of claim 1 , wherein the third statistical model is trained offline to estimate the time in the expected window corresponding to when the engine event occurred.

10. The system of claim 1 , wherein the estimate of the time in the expected window corresponding to when the engine event occurs is within 30 degrees of the true event.

11. A method for training a controller to estimate the timing of an engine event, comprising:

receiving a signal from at least one knock sensor, wherein the signal comprises data corresponding to an engine event;

transforming the signal into a plurality of feature vectors using a multivariate transformation algorithm;

sorting the plurality of feature vectors into a first state and a second state, wherein the first state corresponds to a time before the engine event and the second state corresponds to a time after the engine event; and

forming a first statistical model using the first state;

forming a second statistical model using the second state;

combining the first statistical model and the second statistical model into a third statistical model, wherein the third statistical model is configured to predict when the engine event occurs during normal engine operation.

12. The method of claim 11 , wherein the signal is a one-dimensional, frequency signal.

13. The method of claim 11 , wherein the first statistical model is a first Gaussian Mixture Model and the second statistical model is second Gaussian Mixture Model.

14. The method of claim 13 , wherein the number of Gaussian mixtures of the first Gaussian Mixture Model and the second Gaussian Mixture Model is between 0 and 10.

15. The method of claim 13 , wherein the first state and the second state comprise between 1 and 20 feature vectors.

16. A method for estimating a time of an engine event, comprising:

receiving a first signal from at least one knock sensor coupled to a combustion engine;

receiving a second signal from at least one engine crankshaft sensor coupled to the combustion engine;

transforming the first signal from the at least one knock sensor into a plurality of feature vectors;

sorting, using the second signal from the at least one engine crankshaft sensor, the plurality of feature vectors into a first state and a second state, wherein the first state corresponds to a time fore the engine event and the second state corresponds to a time after the engine event; and

forming a first statistical algorithm using the first state;

forming a second statistical algorithm using the second state;

combining the first statistical algorithm and the second statistical algorithm into a third statistical algorithm, wherein the third statistical algorithm is configured to predict when the engine event occurs during normal engine operation;

determining, using the third statistical algorithm, an expected window of an engine event;

centering, using the third statistical algorithm, a segment of the plurality of feature vectors around the expected window of the engine event by associating a frequency pattern indicative of the engine event with the first signal from the at least one knock sensor;

estimating, using the third statistical algorithm, a time of the engine event; and

outputting a control action for at least the combustion engine based on the time of the engine event.

17. The method of claim 16 , wherein the engine event is a valve closure.

18. The method of claim 16 , wherein the engine event is a mechanical failure of the combustion engine.

19. The method of claim 16 , wherein the statistical algorithm is a Gaussian Mixture Model.

20. The method of claim 16 , wherein the control action comprises shutting down the combustion engine.